Recombinant Research: Sage Congress promotes data sharing in genetics

Report from a movement that believes in open source and open data in science

Given the exponential drop in the cost of personal genome sequencing (you can get a basic DNA test from 23andMe for a couple hundred dollars, and a full sequence will probably soon come down to one thousand dollars in cost), a new dawn seems to be breaking forth for biological research. Yet the assessment of genetics research at the recent Sage Congress was highly cautionary. Various speakers chided their own field for tilling the same ground over and over, ignoring the urgent needs of patients, and just plain researching the wrong things.

Sage Congress also has some plans to fix all that. These projects include tools for sharing data and storing it in cloud facilities, running challenges, injecting new fertility into collaboration projects, and ways to gather more patient data and bring patients into the planning process. Through two days of demos, keynotes, panels, and breakout sessions, Sage Congress brought its vision to a high-level cohort of 230 attendees from universities, pharmaceutical companies, government health agencies, and others who can make change in the field.

In the course of this series of articles, I’ll pinpoint some of the pain points that can force researchers, pharmaceutical companies, doctors, and patients to work together better. I’ll offer a look at the importance of public input, legal frameworks for cooperation, the role of standards, and a number of other topics. But we’ll start by seeing what Sage Bionetworks and its pals have done over the past year.

Synapse: providing the tools for genetics collaboration

Everybody understands that change is driven by people and the culture they form around them, not by tools, but good tools can make it a heck of a lot easier to drive change. To give genetics researchers the best environment available to share their work, Sage Bionetworks created the Synapse platform.

Synapse recognizes that data sets in biological research are getting too large to share through simple data transfers. For instance, in his keynote about cancer research (where he kindly treated us to pictures of cancer victims during lunch), UC Santa Cruz professor David Haussler announced plans to store 25,000 cases at 200 gigabytes per case in the Cancer Genome Atlas, also known as TCGA in what seems to be a clever pun on the four nucleotides in DNA. Storage requirements thus work out to 5 petabytes, which Haussler wants to be expandable to 20 petabytes. In the face of big data like this, the job becomes moving the code to the data, not moving the data to the code.

Synapse points to data sets contributed by cooperating researchers, but also lets you pull up a console in a web browser to run R or Python code on the data. Some effort goes into tagging each data set with associated metadata: tissue type, species tested, last update, number of samples, etc. Thus, you can search across Synapse to find data sets that are pertinent to your research.

One group working with Synapse has already harmonized and normalized the data sets in TCGA so that a researcher can quickly mix and run stats on them to extract emerging patterns. The effort took about one and half full-time employees for six months, but the project leader is confident that with the system in place, “we can activate a similar size repository in hours.”

This contribution highlights an important principle behind Synapse (appropriately called “viral” by some people in the open source movement): when you have manipulated and improved upon the data you find through Synapse, you should put your work back into Synapse. This work could include cleaning up outlier data, adding metadata, and so on. To make work sharing even easier, Synapse has plans to incorporate the Amazon Simple Workflow Service (SWF). It also hopes to add web interfaces to allow non-programmers do do useful work with data.

The Synapse development effort was an impressive one, coming up with a feature-rich Beta version in a year with just four coders. And Synapse code is entirely open source. So not only is the data distributed, but the creators will be happy for research institutions to set up their own Synapse sites. This may make Synapse more appealing to geneticists who are prevented by inertia from visiting the original Synapse.

Mike Kellen, introducing Synapse, compared its potential impact to that of moving research from a world of journals to a world like GitHub, where people record and share every detail of their work and plans. Along these lines, Synapse records who has used a data set. This has many benefits:

Researchers can meet up with others doing related work.

It gives public interest advocates a hook with which to call on those who benefit commercially from Synapse–as we hope the pharmaceutical companies will–to contribute money or other resources.

Members of the public can monitor accesses for suspicious uses that may be unethical.

There’s plenty more work to be done to get data in good shape for sharing. Researchers must agree on some kind of metadata–the dreaded notion of ontologies came up several times–and clean up their data. They must learn about data provenance and versioning.

But sharing is critical for such basics of science as reproducing results. One source estimates that 75% of published results in genetics can’t be replicated. A later article in this series will examine a new model in which enough metainformation is shared about a study for it to be reproduced, and even more important to be a foundation for further research.

With this Beta release of Synapse, Sage Bionetworks feels it is ready for a new initiative to promote collaboration in biological research. But how do you get biologists around the world to start using Synapse? For one, try an activity that’s gotten popular nowadays: a research challenge.

The Sage DREAM challenge

Sage Bionetworks’ DREAM challenge asks genetics researchers to find predictors of the progression of breast cancer. The challenge uses data from 2000 women diagnosed with breast cancer, combining information on DNA alterations affecting how their genes were expressed in the tumors, clinical information about their tumor status, and their outcomes over ten years. The challenge is to build models integrating the alterations with molecular markers and clinical features to predict which women will have the most aggressive disease over a ten year period.

Several hidden aspects of the challenge make it a clever vehicle for Sage Bionetworks’ values and goals. First, breast cancer is a scourge whose urgency is matched by its stubborn resistance to diagnosis. The famous 2009 recommendations of U.S. Preventive Services Task Force, after all the controversy was aired, left us with the dismal truth that we don’t know a good way to predict breast cancer. Some women get mastectomies in the total absence of symptoms based just on frightening family histories. In short, breast cancer puts the research and health care communities in a quandary.

We need finer-grained predictors to say who is likely to get breast cancer, and standard research efforts up to now have fallen short. The Sage proposal is to marshal experts in a new way that combines their strengths, asking them to publish models that show the complex interactions between gene targets and influences from the environment. Sage Bionetworks will publish data sets at regular intervals that it uses to measure the predictive ability of each model. A totally fresh data set will be used at the end to choose the winning model.

The process behind the challenge–particularly the need to upload code in order to run it on the Synapse site–automatically forces model builders to publish all their code. According to Stephen Friend, founder of Sage Bionetworks, “this brings a level of accountability, transparency, and reproducibility not previously achieved in clinical data model challenges.”

Finally, the process has two more effects: it shows off the huge amount of genetic data that can be accessed through Synapse, and it encourages researchers to look at each other’s models in order to boost their own efforts. In less than a month, the challenge already received more than 100 models from 10 sources.

The reward for winning the challenge is publication in a respected journal, the gold medal still sought by academic researchers. (More on shattering this obelisk later in the series.) Science Translational Medicine will accept results of the evaluation as a stand-in for peer review, a real breakthrough for Sage Bionetworks because it validates their software-based, evidence-driven process.

Finally, the DREAM challenge promotes use of the Synapse infrastructure, and in particular the method of bringing the code to the data. Google is donating server space for the challenge, which levels the playing field for researchers, freeing them from paying for their own computing.

A single challenge doesn’t solve all the problems of incentives, of course. We still need to persuade researchers to put up their code and data on a kind of genetic GitHub, persuade pharmaceutical companies to support open research, and persuade the general public to share data about the phonemes (life data) and genes–all topics for upcoming articles in the series.